On Learning of Ceteris Paribus Preference Theories

Abstract

The problem of preference elicitation has been of interest for a long time. While traditional methods of asking a set of relevant questions are still useful, the availability of user-preference data from the web has led to substantial attention to the notion of preference mining. In this thesis, we consider the problem of learning logical preference theories that express preference orderings over alternatives.

We present learning algorithms which accept as input a set of comparisons between pairs of complete descriptions of world states. Our first algorithm, that performs exact learning, accepts the complete set of preference orderings for a theory and generates a theory which provides the same ordering of states as the input. This process can require looking at an exponential number of data points. We then look at more realistic approximation algorithms and analyze the complexity of the learning problem under the framework of Probably Approximately Correct (PAC) learning. We then describe approximation algorithms for learning high-level summaries of the underlying theory.